Strengths and limitations of relative wealth indices derived from big data in Indonesia
Accurate relative wealth estimates in Low and Middle-Income Countries (LMICS) are crucial to help policymakers address socio-demographic inequalities under the guidance of the Sustainable Development Goals set by the United Nations. Survey-based approaches have traditionally been employed to collect...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-02-01
|
| Series: | Frontiers in Big Data |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fdata.2023.1054156/full |
| _version_ | 1797901471572819968 |
|---|---|
| author | Daniele Sartirano Kyriaki Kalimeri Ciro Cattuto Enrique Delamónica Manuel Garcia-Herranz Anthony Mockler Daniela Paolotti Rossano Schifanella Rossano Schifanella |
| author_facet | Daniele Sartirano Kyriaki Kalimeri Ciro Cattuto Enrique Delamónica Manuel Garcia-Herranz Anthony Mockler Daniela Paolotti Rossano Schifanella Rossano Schifanella |
| author_sort | Daniele Sartirano |
| collection | DOAJ |
| description | Accurate relative wealth estimates in Low and Middle-Income Countries (LMICS) are crucial to help policymakers address socio-demographic inequalities under the guidance of the Sustainable Development Goals set by the United Nations. Survey-based approaches have traditionally been employed to collect highly granular data about income, consumption, or household material goods to create index-based poverty estimates. However, these methods are only capture persons in households (i.e., in the household sample framework) and they do not include migrant populations or unhoused citizens. Novel approaches combining frontier data, computer vision, and machine learning have been proposed to complement these existing approaches. However, the strengths and limitations of these big-data-derived indices have yet to be sufficiently studied. In this paper, we focus on the case of Indonesia and examine one frontier-data derived Relative Wealth Index (RWI), created by the Facebook Data for Good initiative, that utilizes connectivity data from the Facebook Platform and satellite imagery data to produce a high-resolution estimate of relative wealth for 135 countries. We examine it concerning asset-based relative wealth indices estimated from existing high-quality national-level traditional survey instruments, the USAID-developed Demographic Health Survey (DHS), and the Indonesian National Socio-economic survey (SUSENAS). In this work, we aim to understand how the frontier-data derived index can be used to inform anti-poverty programs in Indonesia and the Asia Pacific region. First, we unveil key features that affect the comparison between the traditional and non-traditional sources, such as the publishing time and authority and the granularity of the spatial aggregation of the data. Second, to provide operational input, we hypothesize how a re-distribution of resources based on the RWI map would impact a current social program, the Social Protection Card (KPS) of Indonesia and assess impact. In this hypothetical scenario, we estimate the percentage of Indonesians eligible for the program, which would have been incorrectly excluded from a social protection payment had the RWI been used in place of the survey-based wealth index. The exclusion error in that case would be 32.82%. Within the context of the KPS program targeting, we noted significant differences between the RWI map's predictions and the SUSENAS ground truth index estimates. |
| first_indexed | 2024-04-10T09:01:27Z |
| format | Article |
| id | doaj.art-bcb444ed9a214b0a9845b5d16c54c831 |
| institution | Directory Open Access Journal |
| issn | 2624-909X |
| language | English |
| last_indexed | 2024-04-10T09:01:27Z |
| publishDate | 2023-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Big Data |
| spelling | doaj.art-bcb444ed9a214b0a9845b5d16c54c8312023-02-21T13:33:43ZengFrontiers Media S.A.Frontiers in Big Data2624-909X2023-02-01610.3389/fdata.2023.10541561054156Strengths and limitations of relative wealth indices derived from big data in IndonesiaDaniele Sartirano0Kyriaki Kalimeri1Ciro Cattuto2Enrique Delamónica3Manuel Garcia-Herranz4Anthony Mockler5Daniela Paolotti6Rossano Schifanella7Rossano Schifanella8ISI Foundation, Turin, ItalyISI Foundation, Turin, ItalyISI Foundation, Turin, ItalyUNICEF, New York, NY, United StatesUNICEF, New York, NY, United StatesUNICEF, Jakarta, IndonesiaISI Foundation, Turin, ItalyISI Foundation, Turin, ItalyDepartment of Computer Science, University of Turin, Turin, ItalyAccurate relative wealth estimates in Low and Middle-Income Countries (LMICS) are crucial to help policymakers address socio-demographic inequalities under the guidance of the Sustainable Development Goals set by the United Nations. Survey-based approaches have traditionally been employed to collect highly granular data about income, consumption, or household material goods to create index-based poverty estimates. However, these methods are only capture persons in households (i.e., in the household sample framework) and they do not include migrant populations or unhoused citizens. Novel approaches combining frontier data, computer vision, and machine learning have been proposed to complement these existing approaches. However, the strengths and limitations of these big-data-derived indices have yet to be sufficiently studied. In this paper, we focus on the case of Indonesia and examine one frontier-data derived Relative Wealth Index (RWI), created by the Facebook Data for Good initiative, that utilizes connectivity data from the Facebook Platform and satellite imagery data to produce a high-resolution estimate of relative wealth for 135 countries. We examine it concerning asset-based relative wealth indices estimated from existing high-quality national-level traditional survey instruments, the USAID-developed Demographic Health Survey (DHS), and the Indonesian National Socio-economic survey (SUSENAS). In this work, we aim to understand how the frontier-data derived index can be used to inform anti-poverty programs in Indonesia and the Asia Pacific region. First, we unveil key features that affect the comparison between the traditional and non-traditional sources, such as the publishing time and authority and the granularity of the spatial aggregation of the data. Second, to provide operational input, we hypothesize how a re-distribution of resources based on the RWI map would impact a current social program, the Social Protection Card (KPS) of Indonesia and assess impact. In this hypothetical scenario, we estimate the percentage of Indonesians eligible for the program, which would have been incorrectly excluded from a social protection payment had the RWI been used in place of the survey-based wealth index. The exclusion error in that case would be 32.82%. Within the context of the KPS program targeting, we noted significant differences between the RWI map's predictions and the SUSENAS ground truth index estimates.https://www.frontiersin.org/articles/10.3389/fdata.2023.1054156/fullwealthindexpovertysurveymachine learning |
| spellingShingle | Daniele Sartirano Kyriaki Kalimeri Ciro Cattuto Enrique Delamónica Manuel Garcia-Herranz Anthony Mockler Daniela Paolotti Rossano Schifanella Rossano Schifanella Strengths and limitations of relative wealth indices derived from big data in Indonesia Frontiers in Big Data wealth index poverty survey machine learning |
| title | Strengths and limitations of relative wealth indices derived from big data in Indonesia |
| title_full | Strengths and limitations of relative wealth indices derived from big data in Indonesia |
| title_fullStr | Strengths and limitations of relative wealth indices derived from big data in Indonesia |
| title_full_unstemmed | Strengths and limitations of relative wealth indices derived from big data in Indonesia |
| title_short | Strengths and limitations of relative wealth indices derived from big data in Indonesia |
| title_sort | strengths and limitations of relative wealth indices derived from big data in indonesia |
| topic | wealth index poverty survey machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fdata.2023.1054156/full |
| work_keys_str_mv | AT danielesartirano strengthsandlimitationsofrelativewealthindicesderivedfrombigdatainindonesia AT kyriakikalimeri strengthsandlimitationsofrelativewealthindicesderivedfrombigdatainindonesia AT cirocattuto strengthsandlimitationsofrelativewealthindicesderivedfrombigdatainindonesia AT enriquedelamonica strengthsandlimitationsofrelativewealthindicesderivedfrombigdatainindonesia AT manuelgarciaherranz strengthsandlimitationsofrelativewealthindicesderivedfrombigdatainindonesia AT anthonymockler strengthsandlimitationsofrelativewealthindicesderivedfrombigdatainindonesia AT danielapaolotti strengthsandlimitationsofrelativewealthindicesderivedfrombigdatainindonesia AT rossanoschifanella strengthsandlimitationsofrelativewealthindicesderivedfrombigdatainindonesia AT rossanoschifanella strengthsandlimitationsofrelativewealthindicesderivedfrombigdatainindonesia |